Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations3512392
Missing cells0
Missing cells (%)0.0%
Duplicate rows1300
Duplicate rows (%)< 0.1%
Total size in memory562.7 MiB
Average record size in memory168.0 B

Variable types

Numeric10
Categorical11

Alerts

Dataset has 1300 (< 0.1%) duplicate rowsDuplicates
Crossing is highly overall correlated with Traffic_SignalHigh correlation
Traffic_Signal is highly overall correlated with CrossingHigh correlation
Amenity is highly imbalanced (96.3%) Imbalance
Crossing is highly imbalanced (65.2%) Imbalance
Give_Way is highly imbalanced (97.9%) Imbalance
Junction is highly imbalanced (73.4%) Imbalance
No_Exit is highly imbalanced (98.9%) Imbalance
Railway is highly imbalanced (96.5%) Imbalance
Stop is highly imbalanced (92.7%) Imbalance
Traffic_Calming is highly imbalanced (99.5%) Imbalance
Traffic_Signal is highly imbalanced (52.7%) Imbalance
Duration_Seconds is highly skewed (γ1 = 44.26266856) Skewed
Severity is uniformly distributed Uniform

Reproduction

Analysis started2024-11-27 16:06:49.135022
Analysis finished2024-11-27 16:10:05.007769
Duration3 minutes and 15.87 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Distance(mi)
Real number (ℝ)

Distinct1124405
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6611225 × 10-16
Minimum-0.31162571
Maximum216.3573
Zeros0
Zeros (%)0.0%
Negative2745017
Negative (%)78.2%
Memory size26.8 MiB
2024-11-27T17:10:05.083757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.31162571
5-th percentile-0.31162571
Q1-0.31162571
median-0.30772302
Q3-0.064424494
95-th percentile1.173923
Maximum216.3573
Range216.66892
Interquartile range (IQR)0.24720121

Descriptive statistics

Standard deviation1.0000001
Coefficient of variation (CV)3.7578132 × 1015
Kurtosis1673.9463
Mean2.6611225 × 10-16
Median Absolute Deviation (MAD)0.0039026897
Skewness19.924736
Sum1.6734703 × 10-9
Variance1.0000003
MonotonicityNot monotonic
2024-11-27T17:10:05.204753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.311625708 1711584
48.7%
-0.306720922 84939
 
2.4%
-0.3067209221 6100
 
0.2%
-0.3077018792 1387
 
< 0.1%
-0.3072114006 1315
 
< 0.1%
-0.3062304434 1265
 
< 0.1%
-0.2983827858 1231
 
< 0.1%
-0.301816136 1228
 
< 0.1%
-0.29691135 1224
 
< 0.1%
-0.299363743 1186
 
< 0.1%
Other values (1124395) 1700933
48.4%
ValueCountFrequency (%)
-0.311625708 1711584
48.7%
-0.3116256961 1
 
< 0.1%
-0.3116256403 1
 
< 0.1%
-0.3116255749 1
 
< 0.1%
-0.3116255307 1
 
< 0.1%
-0.3116254697 1
 
< 0.1%
-0.3116254237 1
 
< 0.1%
-0.3116253982 1
 
< 0.1%
-0.3116253406 1
 
< 0.1%
-0.311625202 1
 
< 0.1%
ValueCountFrequency (%)
216.3572954 1
< 0.1%
164.7687599 1
< 0.1%
163.3267516 1
< 0.1%
124.4661269 1
< 0.1%
122.9064085 1
< 0.1%
118.5509562 1
< 0.1%
102.7281192 1
< 0.1%
89.50481293 1
< 0.1%
85.85565095 1
< 0.1%
79.39114661 2
< 0.1%

Temperature(F)
Real number (ℝ)

Distinct1875643
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2629742 × 10-15
Minimum-8.6163943
Maximum8.0760202
Zeros0
Zeros (%)0.0%
Negative1545484
Negative (%)44.0%
Memory size26.8 MiB
2024-11-27T17:10:05.316798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8.6163943
5-th percentile-1.8548385
Q1-0.60854677
median0.15155366
Q30.73909829
95-th percentile1.3787944
Maximum8.0760202
Range16.692414
Interquartile range (IQR)1.3476451

Descriptive statistics

Standard deviation1.0000001
Coefficient of variation (CV)-7.9178194 × 1014
Kurtosis0.1867772
Mean-1.2629742 × 10-15
Median Absolute Deviation (MAD)0.64973187
Skewness-0.61446997
Sum-4.7803042 × 10-9
Variance1.0000003
MonotonicityNot monotonic
2024-11-27T17:10:05.431811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7448922394 38816
 
1.1%
0.5193190706 38549
 
1.1%
0.2373526095 36664
 
1.0%
0.4629257784 35628
 
1.0%
0.632105655 35507
 
1.0%
0.350139194 33937
 
1.0%
0.8576788239 32098
 
0.9%
-0.2701870204 32045
 
0.9%
0.01177944069 31511
 
0.9%
-0.04461385153 31505
 
0.9%
Other values (1875633) 3166132
90.1%
ValueCountFrequency (%)
-8.616394268 4
 
< 0.1%
-5.853122949 1
 
< 0.1%
-5.841196849 1
 
< 0.1%
-5.683943073 1
 
< 0.1%
-5.571156488 23
< 0.1%
-5.565517195 1
 
< 0.1%
-5.545226452 1
 
< 0.1%
-5.531422643 1
 
< 0.1%
-5.493761139 1
 
< 0.1%
-5.447091245 1
 
< 0.1%
ValueCountFrequency (%)
8.076020227 1
 
< 0.1%
7.060940967 1
 
< 0.1%
6.636863831 1
 
< 0.1%
6.102255 1
 
< 0.1%
6.023304391 1
 
< 0.1%
5.538322077 1
 
< 0.1%
4.87229758 1
 
< 0.1%
4.727002506 1
 
< 0.1%
4.564224822 1
 
< 0.1%
4.297669649 3
< 0.1%

Humidity(%)
Real number (ℝ)

Distinct1856469
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.0367805 × 10-15
Minimum-2.8388714
Maximum1.542379
Zeros0
Zeros (%)0.0%
Negative1622247
Negative (%)46.2%
Memory size26.8 MiB
2024-11-27T17:10:05.577812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.8388714
5-th percentile-1.8652602
Q1-0.71207283
median0.12621722
Q30.83429809
95-th percentile1.3653587
Maximum1.542379
Range4.3812504
Interquartile range (IQR)1.5463709

Descriptive statistics

Standard deviation1.0000001
Coefficient of variation (CV)-4.9097098 × 1014
Kurtosis-0.58601353
Mean-2.0367805 × 10-15
Median Absolute Deviation (MAD)0.75233592
Skewness-0.49340084
Sum-6.522896 × 10-9
Variance1.0000003
MonotonicityNot monotonic
2024-11-27T17:10:05.704826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.542378954 73331
 
2.1%
1.232593574 66741
 
1.9%
1.099828411 39481
 
1.1%
0.9670632481 39423
 
1.1%
1.365358737 30538
 
0.9%
1.055573357 29930
 
0.9%
0.8342980852 29815
 
0.8%
1.276848628 29646
 
0.8%
0.7015329223 27940
 
0.8%
0.5687677594 27126
 
0.8%
Other values (1856459) 3118421
88.8%
ValueCountFrequency (%)
-2.838871421 5
 
< 0.1%
-2.794616367 32
< 0.1%
-2.790840726 1
 
< 0.1%
-2.786778631 1
 
< 0.1%
-2.785544517 1
 
< 0.1%
-2.780980555 1
 
< 0.1%
-2.775682035 1
 
< 0.1%
-2.774477351 1
 
< 0.1%
-2.770729633 1
 
< 0.1%
-2.765302559 1
 
< 0.1%
ValueCountFrequency (%)
1.542378954 73331
2.1%
1.542375543 1
 
< 0.1%
1.542374991 1
 
< 0.1%
1.542374647 1
 
< 0.1%
1.542372725 1
 
< 0.1%
1.542368432 1
 
< 0.1%
1.542366887 1
 
< 0.1%
1.542363032 1
 
< 0.1%
1.542359929 1
 
< 0.1%
1.542358175 1
 
< 0.1%

Pressure(in)
Real number (ℝ)

Distinct1915329
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1373418 × 10-14
Minimum-29.716689
Maximum29.375982
Zeros0
Zeros (%)0.0%
Negative1169583
Negative (%)33.3%
Memory size26.8 MiB
2024-11-27T17:10:05.821461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-29.716689
5-th percentile-1.9473088
Q1-0.18562827
median0.31089211
Q30.53144915
95-th percentile0.76140229
Maximum29.375982
Range59.092671
Interquartile range (IQR)0.71707743

Descriptive statistics

Standard deviation1.0000001
Coefficient of variation (CV)3.1874122 × 1013
Kurtosis17.893053
Mean3.1373418 × 10-14
Median Absolute Deviation (MAD)0.29412541
Skewness-3.3782853
Sum1.1045995 × 10-7
Variance1.0000003
MonotonicityNot monotonic
2024-11-27T17:10:05.939069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4966410593 28002
 
0.8%
0.5271904324 27744
 
0.8%
0.5475566812 27358
 
0.8%
0.4762748106 26669
 
0.8%
0.5781060542 26461
 
0.8%
0.598472303 25088
 
0.7%
0.4457254375 24648
 
0.7%
0.5068241837 24570
 
0.7%
0.5373735568 24557
 
0.7%
0.5679229299 24540
 
0.7%
Other values (1915319) 3252755
92.6%
ValueCountFrequency (%)
-29.71668892 1
 
< 0.1%
-29.7065058 3
< 0.1%
-26.96724535 1
 
< 0.1%
-26.95706222 2
< 0.1%
-26.9468791 2
< 0.1%
-18.19613393 1
 
< 0.1%
-12.99599872 1
 
< 0.1%
-12.88398435 1
 
< 0.1%
-10.28728764 1
 
< 0.1%
-10.13454078 1
 
< 0.1%
ValueCountFrequency (%)
29.37598175 1
< 0.1%
29.15195302 1
< 0.1%
29.09085427 1
< 0.1%
28.58169805 1
< 0.1%
26.50219086 1
< 0.1%
24.83931437 1
< 0.1%
23.71416461 1
< 0.1%
10.95011425 1
< 0.1%
10.16042608 1
< 0.1%
9.88903008 1
< 0.1%

Visibility(mi)
Real number (ℝ)

Distinct509336
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2649162 × 10-16
Minimum-3.8777941
Maximum42.894767
Zeros0
Zeros (%)0.0%
Negative694537
Negative (%)19.8%
Memory size26.8 MiB
2024-11-27T17:10:06.056069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.8777941
5-th percentile-2.2359955
Q10.33595008
median0.33595008
Q30.33595008
95-th percentile0.33595008
Maximum42.894767
Range46.772561
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0000001
Coefficient of variation (CV)-7.905663 × 1015
Kurtosis80.165104
Mean-1.2649162 × 10-16
Median Absolute Deviation (MAD)0
Skewness2.193761
Sum8.1490725 × 10-10
Variance1.0000003
MonotonicityNot monotonic
2024-11-27T17:10:06.172069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3359500846 2670321
76.0%
-0.928173179 49520
 
1.4%
-0.08542433662 40285
 
1.1%
-0.5067987578 32304
 
0.9%
-1.770922021 32192
 
0.9%
-1.3495476 27583
 
0.8%
-2.192296443 26760
 
0.8%
-2.613670864 26483
 
0.8%
-3.035045285 25481
 
0.7%
-3.456419706 19807
 
0.6%
Other values (509326) 561656
 
16.0%
ValueCountFrequency (%)
-3.877794127 1153
< 0.1%
-3.876701845 1
 
< 0.1%
-3.876467326 1
 
< 0.1%
-3.876173263 1
 
< 0.1%
-3.875722741 1
 
< 0.1%
-3.87521848 1
 
< 0.1%
-3.875119917 1
 
< 0.1%
-3.874803376 1
 
< 0.1%
-3.874690077 1
 
< 0.1%
-3.874613379 1
 
< 0.1%
ValueCountFrequency (%)
42.89476662 1
 
< 0.1%
38.25964799 6
 
< 0.1%
34.04590378 3
 
< 0.1%
31.9777354 1
 
< 0.1%
29.83215957 32
< 0.1%
29.13729807 1
 
< 0.1%
27.72528746 55
< 0.1%
27.66837187 1
 
< 0.1%
27.60820874 1
 
< 0.1%
26.98868201 1
 
< 0.1%

Wind_Direction
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4574385 × 10-17
Minimum-1.4647811
Maximum1.7873691
Zeros0
Zeros (%)0.0%
Negative1668072
Negative (%)47.5%
Memory size26.8 MiB
2024-11-27T17:10:06.269068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.4647811
5-th percentile-1.4647811
Q1-0.89087228
median0.065642487
Q30.8308543
95-th percentile1.5960661
Maximum1.7873691
Range3.2521502
Interquartile range (IQR)1.7217266

Descriptive statistics

Standard deviation1.0000001
Coefficient of variation (CV)1.3409432 × 1016
Kurtosis-1.2184889
Mean7.4574385 × 10-17
Median Absolute Deviation (MAD)0.76521182
Skewness0.012854768
Sum3.1159608 × 10-10
Variance1.0000003
MonotonicityNot monotonic
2024-11-27T17:10:06.359069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
-1.464781148 470867
 
13.4%
0.2569454415 268174
 
7.6%
0.8308543048 207207
 
5.9%
1.404763168 206835
 
5.9%
-0.6995693305 204385
 
5.8%
0.06564248714 202253
 
5.8%
0.6395513504 195316
 
5.6%
1.022157259 192201
 
5.5%
-0.1256604673 186981
 
5.3%
0.4482483959 182203
 
5.2%
Other values (8) 1195970
34.1%
ValueCountFrequency (%)
-1.464781148 470867
13.4%
-1.273478194 181410
 
5.2%
-1.082175239 150971
 
4.3%
-0.8908722849 155168
 
4.4%
-0.6995693305 204385
5.8%
-0.5082663761 158365
 
4.5%
-0.3169634217 159925
 
4.6%
-0.1256604673 186981
 
5.3%
0.06564248714 202253
5.8%
0.2569454415 268174
7.6%
ValueCountFrequency (%)
1.787369077 84354
 
2.4%
1.596066122 131332
3.7%
1.404763168 206835
5.9%
1.213460214 174445
5.0%
1.022157259 192201
5.5%
0.8308543048 207207
5.9%
0.6395513504 195316
5.6%
0.4482483959 182203
5.2%
0.2569454415 268174
7.6%
0.06564248714 202253
5.8%

Wind_Speed(mph)
Real number (ℝ)

Distinct1712599
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.0416201 × 10-16
Minimum-1.6227425
Maximum174.03158
Zeros0
Zeros (%)0.0%
Negative1846147
Negative (%)52.6%
Memory size26.8 MiB
2024-11-27T17:10:06.476071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.6227425
5-th percentile-1.6227425
Q1-0.59625572
median-0.097809402
Q30.51209365
95-th percentile1.7929953
Maximum174.03158
Range175.65432
Interquartile range (IQR)1.1083494

Descriptive statistics

Standard deviation1.0000001
Coefficient of variation (CV)-1.9834897 × 1015
Kurtosis604.92832
Mean-5.0416201 × 10-16
Median Absolute Deviation (MAD)0.54290847
Skewness4.7633551
Sum4.7293724 × 10-11
Variance1.0000003
MonotonicityNot monotonic
2024-11-27T17:10:06.598070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.622742498 230012
 
6.5%
0.01798359913 134474
 
3.8%
-0.555324426 110154
 
3.1%
-0.3418408117 106254
 
3.0%
-0.9822916546 104446
 
3.0%
-0.1283571973 98645
 
2.8%
0.08512641699 87010
 
2.5%
0.2986100313 77979
 
2.2%
-0.6407178717 68009
 
1.9%
-0.3845375345 67103
 
1.9%
Other values (1712589) 2428306
69.1%
ValueCountFrequency (%)
-1.622742498 230012
6.5%
-1.622740869 1
 
< 0.1%
-1.62274072 1
 
< 0.1%
-1.622734507 1
 
< 0.1%
-1.622731966 1
 
< 0.1%
-1.622729354 1
 
< 0.1%
-1.622727059 1
 
< 0.1%
-1.622726042 1
 
< 0.1%
-1.622723348 1
 
< 0.1%
-1.622716321 1
 
< 0.1%
ValueCountFrequency (%)
174.0315754 2
< 0.1%
122.1977538 1
< 0.1%
52.81557915 1
< 0.1%
50.25377578 1
< 0.1%
47.90545603 1
< 0.1%
47.4784888 1
< 0.1%
45.65925011 1
< 0.1%
45.46710683 1
< 0.1%
44.06275097 2
< 0.1%
35.73689001 1
< 0.1%

Weather_Condition
Real number (ℝ)

Distinct143
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3144171 × 10-17
Minimum-1.1493993
Maximum2.7579964
Zeros0
Zeros (%)0.0%
Negative2069211
Negative (%)58.9%
Memory size26.8 MiB
2024-11-27T17:10:06.720453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1493993
5-th percentile-0.98429812
Q1-0.76416315
median-0.73664628
Q31.1620179
95-th percentile1.354636
Maximum2.7579964
Range3.9073957
Interquartile range (IQR)1.926181

Descriptive statistics

Standard deviation1.0000001
Coefficient of variation (CV)3.0171222 × 1016
Kurtosis-1.5040685
Mean3.3144171 × 10-17
Median Absolute Deviation (MAD)0.24765184
Skewness0.48836104
Sum1.5962609 × 10-10
Variance1.0000003
MonotonicityNot monotonic
2024-11-27T17:10:06.844201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7366462752 1022804
29.1%
1.162017856 392458
 
11.2%
-0.9842981184 364698
 
10.4%
-0.956781247 253823
 
7.2%
1.327119085 213430
 
6.1%
1.244568471 184271
 
5.2%
0.5291298124 111245
 
3.2%
1.739872157 76222
 
2.2%
1.189534728 44991
 
1.3%
-0.9292643755 43707
 
1.2%
Other values (133) 804743
22.9%
ValueCountFrequency (%)
-1.149399347 20
 
< 0.1%
-1.121882476 79
 
< 0.1%
-1.094365604 24
 
< 0.1%
-1.066848733 207
 
< 0.1%
-1.039331861 222
 
< 0.1%
-1.01181499 135
 
< 0.1%
-0.9842981184 364698
10.4%
-0.956781247 253823
7.2%
-0.9292643755 43707
 
1.2%
-0.901747504 40566
 
1.2%
ValueCountFrequency (%)
2.757996401 58
 
< 0.1%
2.73047953 1883
0.1%
2.702962658 57
 
< 0.1%
2.675445787 89
 
< 0.1%
2.647928915 72
 
< 0.1%
2.620412044 59
 
< 0.1%
2.592895173 69
 
< 0.1%
2.565378301 835
 
< 0.1%
2.53786143 1602
< 0.1%
2.510344558 3382
0.1%

Amenity
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
-0.06321243402403585
3498413 
15.819672433745573
 
13979

Length

Max length20
Median length20
Mean length19.99204
Min length18

Characters and Unicode

Total characters70219882
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.06321243402403585
2nd row-0.06321243402403585
3rd row-0.06321243402403585
4th row-0.06321243402403585
5th row-0.06321243402403585

Common Values

ValueCountFrequency (%)
-0.06321243402403585 3498413
99.6%
15.819672433745573 13979
 
0.4%

Length

2024-11-27T17:10:06.968796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T17:10:07.059803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.06321243402403585 3498413
99.6%
15.819672433745573 13979
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 13993652
19.9%
3 10537176
15.0%
4 10523197
15.0%
2 10509218
15.0%
5 7038763
10.0%
1 3526371
 
5.0%
. 3512392
 
5.0%
6 3512392
 
5.0%
8 3512392
 
5.0%
- 3498413
 
5.0%
Other values (2) 55916
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63209077
90.0%
Other Punctuation 3512392
 
5.0%
Dash Punctuation 3498413
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13993652
22.1%
3 10537176
16.7%
4 10523197
16.6%
2 10509218
16.6%
5 7038763
11.1%
1 3526371
 
5.6%
6 3512392
 
5.6%
8 3512392
 
5.6%
7 41937
 
0.1%
9 13979
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 3512392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3498413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70219882
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13993652
19.9%
3 10537176
15.0%
4 10523197
15.0%
2 10509218
15.0%
5 7038763
10.0%
1 3526371
 
5.0%
. 3512392
 
5.0%
6 3512392
 
5.0%
8 3512392
 
5.0%
- 3498413
 
5.0%
Other values (2) 55916
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70219882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13993652
19.9%
3 10537176
15.0%
4 10523197
15.0%
2 10509218
15.0%
5 7038763
10.0%
1 3526371
 
5.0%
. 3512392
 
5.0%
6 3512392
 
5.0%
8 3512392
 
5.0%
- 3498413
 
5.0%
Other values (2) 55916
 
0.1%

Crossing
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
-0.2642536442661127
3283131 
3.7842429866093523
 
229261

Length

Max length19
Median length19
Mean length18.934728
Min length18

Characters and Unicode

Total characters66506187
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.2642536442661127
2nd row-0.2642536442661127
3rd row3.7842429866093523
4th row-0.2642536442661127
5th row3.7842429866093523

Common Values

ValueCountFrequency (%)
-0.2642536442661127 3283131
93.5%
3.7842429866093523 229261
 
6.5%

Length

2024-11-27T17:10:07.155787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T17:10:07.243809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.2642536442661127 3283131
93.5%
3.7842429866093523 229261
 
6.5%

Most occurring characters

ValueCountFrequency (%)
2 13820307
20.8%
6 13591046
20.4%
4 10307915
15.5%
1 6566262
9.9%
3 3970914
 
6.0%
0 3512392
 
5.3%
. 3512392
 
5.3%
5 3512392
 
5.3%
7 3512392
 
5.3%
- 3283131
 
4.9%
Other values (2) 917044
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59710664
89.8%
Other Punctuation 3512392
 
5.3%
Dash Punctuation 3283131
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 13820307
23.1%
6 13591046
22.8%
4 10307915
17.3%
1 6566262
11.0%
3 3970914
 
6.7%
0 3512392
 
5.9%
5 3512392
 
5.9%
7 3512392
 
5.9%
8 458522
 
0.8%
9 458522
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 3512392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3283131
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66506187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 13820307
20.8%
6 13591046
20.4%
4 10307915
15.5%
1 6566262
9.9%
3 3970914
 
6.0%
0 3512392
 
5.3%
. 3512392
 
5.3%
5 3512392
 
5.3%
7 3512392
 
5.3%
- 3283131
 
4.9%
Other values (2) 917044
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66506187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 13820307
20.8%
6 13591046
20.4%
4 10307915
15.5%
1 6566262
9.9%
3 3970914
 
6.0%
0 3512392
 
5.3%
. 3512392
 
5.3%
5 3512392
 
5.3%
7 3512392
 
5.3%
- 3283131
 
4.9%
Other values (2) 917044
 
1.4%

Give_Way
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
-0.04440461591472006
3505480 
22.52018127846251
 
6912

Length

Max length20
Median length20
Mean length19.994096
Min length17

Characters and Unicode

Total characters70227104
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.04440461591472006
2nd row-0.04440461591472006
3rd row-0.04440461591472006
4th row-0.04440461591472006
5th row-0.04440461591472006

Common Values

ValueCountFrequency (%)
-0.04440461591472006 3505480
99.8%
22.52018127846251 6912
 
0.2%

Length

2024-11-27T17:10:07.343797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T17:10:07.437797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.04440461591472006 3505480
99.8%
22.52018127846251 6912
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 17534312
25.0%
4 17534312
25.0%
1 7031696
10.0%
6 7017872
10.0%
2 3540040
 
5.0%
5 3519304
 
5.0%
. 3512392
 
5.0%
7 3512392
 
5.0%
- 3505480
 
5.0%
9 3505480
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63209232
90.0%
Other Punctuation 3512392
 
5.0%
Dash Punctuation 3505480
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17534312
27.7%
4 17534312
27.7%
1 7031696
11.1%
6 7017872
11.1%
2 3540040
 
5.6%
5 3519304
 
5.6%
7 3512392
 
5.6%
9 3505480
 
5.5%
8 13824
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 3512392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3505480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70227104
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17534312
25.0%
4 17534312
25.0%
1 7031696
10.0%
6 7017872
10.0%
2 3540040
 
5.0%
5 3519304
 
5.0%
. 3512392
 
5.0%
7 3512392
 
5.0%
- 3505480
 
5.0%
9 3505480
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70227104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17534312
25.0%
4 17534312
25.0%
1 7031696
10.0%
6 7017872
10.0%
2 3540040
 
5.0%
5 3519304
 
5.0%
. 3512392
 
5.0%
7 3512392
 
5.0%
- 3505480
 
5.0%
9 3505480
 
5.0%

Junction
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
-0.21756616363716788
3353647 
4.596302951168838
 
158745

Length

Max length20
Median length20
Mean length19.864413
Min length17

Characters and Unicode

Total characters69771605
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.21756616363716788
2nd row-0.21756616363716788
3rd row-0.21756616363716788
4th row-0.21756616363716788
5th row-0.21756616363716788

Common Values

ValueCountFrequency (%)
-0.21756616363716788 3353647
95.5%
4.596302951168838 158745
 
4.5%

Length

2024-11-27T17:10:07.539811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T17:10:07.629337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.21756616363716788 3353647
95.5%
4.596302951168838 158745
 
4.5%

Most occurring characters

ValueCountFrequency (%)
6 17085725
24.5%
1 10378431
14.9%
7 10060941
14.4%
8 7183529
10.3%
3 7024784
10.1%
5 3671137
 
5.3%
0 3512392
 
5.0%
. 3512392
 
5.0%
2 3512392
 
5.0%
- 3353647
 
4.8%
Other values (2) 476235
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62905566
90.2%
Other Punctuation 3512392
 
5.0%
Dash Punctuation 3353647
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 17085725
27.2%
1 10378431
16.5%
7 10060941
16.0%
8 7183529
11.4%
3 7024784
11.2%
5 3671137
 
5.8%
0 3512392
 
5.6%
2 3512392
 
5.6%
9 317490
 
0.5%
4 158745
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 3512392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3353647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69771605
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 17085725
24.5%
1 10378431
14.9%
7 10060941
14.4%
8 7183529
10.3%
3 7024784
10.1%
5 3671137
 
5.3%
0 3512392
 
5.0%
. 3512392
 
5.0%
2 3512392
 
5.0%
- 3353647
 
4.8%
Other values (2) 476235
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69771605
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 17085725
24.5%
1 10378431
14.9%
7 10060941
14.4%
8 7183529
10.3%
3 7024784
10.1%
5 3671137
 
5.3%
0 3512392
 
5.0%
. 3512392
 
5.0%
2 3512392
 
5.0%
- 3353647
 
4.8%
Other values (2) 476235
 
0.7%

No_Exit
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
-0.030856267985433485
3509051 
32.408326258770835
 
3341

Length

Max length21
Median length21
Mean length20.997146
Min length18

Characters and Unicode

Total characters73750209
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.030856267985433485
2nd row-0.030856267985433485
3rd row-0.030856267985433485
4th row-0.030856267985433485
5th row-0.030856267985433485

Common Values

ValueCountFrequency (%)
-0.030856267985433485 3509051
99.9%
32.408326258770835 3341
 
0.1%

Length

2024-11-27T17:10:07.731333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T17:10:07.824953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.030856267985433485 3509051
99.9%
32.408326258770835 3341
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 10537176
14.3%
8 10537176
14.3%
0 10533835
14.3%
5 10533835
14.3%
6 7021443
9.5%
4 7021443
9.5%
2 3519074
 
4.8%
7 3515733
 
4.8%
. 3512392
 
4.8%
- 3509051
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66728766
90.5%
Other Punctuation 3512392
 
4.8%
Dash Punctuation 3509051
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 10537176
15.8%
8 10537176
15.8%
0 10533835
15.8%
5 10533835
15.8%
6 7021443
10.5%
4 7021443
10.5%
2 3519074
 
5.3%
7 3515733
 
5.3%
9 3509051
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 3512392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3509051
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73750209
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 10537176
14.3%
8 10537176
14.3%
0 10533835
14.3%
5 10533835
14.3%
6 7021443
9.5%
4 7021443
9.5%
2 3519074
 
4.8%
7 3515733
 
4.8%
. 3512392
 
4.8%
- 3509051
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73750209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 10537176
14.3%
8 10537176
14.3%
0 10533835
14.3%
5 10533835
14.3%
6 7021443
9.5%
4 7021443
9.5%
2 3519074
 
4.8%
7 3515733
 
4.8%
. 3512392
 
4.8%
- 3509051
 
4.8%

Railway
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
-0.0611780507827015
3499295 
16.345731634240927
 
13097

Length

Max length19
Median length19
Mean length18.996271
Min length18

Characters and Unicode

Total characters66722351
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.0611780507827015
2nd row-0.0611780507827015
3rd row-0.0611780507827015
4th row-0.0611780507827015
5th row-0.0611780507827015

Common Values

ValueCountFrequency (%)
-0.0611780507827015 3499295
99.6%
16.345731634240927 13097
 
0.4%

Length

2024-11-27T17:10:07.931124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T17:10:08.019143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0611780507827015 3499295
99.6%
16.345731634240927 13097
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 17509572
26.2%
1 10524079
15.8%
7 10524079
15.8%
5 7011687
10.5%
8 6998590
 
10.5%
6 3525489
 
5.3%
2 3525489
 
5.3%
. 3512392
 
5.3%
- 3499295
 
5.2%
3 39291
 
0.1%
Other values (2) 52388
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59710664
89.5%
Other Punctuation 3512392
 
5.3%
Dash Punctuation 3499295
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17509572
29.3%
1 10524079
17.6%
7 10524079
17.6%
5 7011687
11.7%
8 6998590
 
11.7%
6 3525489
 
5.9%
2 3525489
 
5.9%
3 39291
 
0.1%
4 39291
 
0.1%
9 13097
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 3512392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3499295
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66722351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17509572
26.2%
1 10524079
15.8%
7 10524079
15.8%
5 7011687
10.5%
8 6998590
 
10.5%
6 3525489
 
5.3%
2 3525489
 
5.3%
. 3512392
 
5.3%
- 3499295
 
5.2%
3 39291
 
0.1%
Other values (2) 52388
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66722351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17509572
26.2%
1 10524079
15.8%
7 10524079
15.8%
5 7011687
10.5%
8 6998590
 
10.5%
6 3525489
 
5.3%
2 3525489
 
5.3%
. 3512392
 
5.3%
- 3499295
 
5.2%
3 39291
 
0.1%
Other values (2) 52388
 
0.1%

Stop
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
-0.09412835311470476
3481545 
10.623791524126649
 
30847

Length

Max length20
Median length20
Mean length19.982435
Min length18

Characters and Unicode

Total characters70186146
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.09412835311470476
2nd row-0.09412835311470476
3rd row-0.09412835311470476
4th row-0.09412835311470476
5th row-0.09412835311470476

Common Values

ValueCountFrequency (%)
-0.09412835311470476 3481545
99.1%
10.623791524126649 30847
 
0.9%

Length

2024-11-27T17:10:08.128145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T17:10:08.222145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.09412835311470476 3481545
99.1%
10.623791524126649 30847
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 10537176
15.0%
4 10506329
15.0%
0 10475482
14.9%
3 6993937
10.0%
7 6993937
10.0%
2 3574086
 
5.1%
6 3574086
 
5.1%
9 3543239
 
5.0%
. 3512392
 
5.0%
5 3512392
 
5.0%
Other values (2) 6963090
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63192209
90.0%
Other Punctuation 3512392
 
5.0%
Dash Punctuation 3481545
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10537176
16.7%
4 10506329
16.6%
0 10475482
16.6%
3 6993937
11.1%
7 6993937
11.1%
2 3574086
 
5.7%
6 3574086
 
5.7%
9 3543239
 
5.6%
5 3512392
 
5.6%
8 3481545
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 3512392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3481545
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70186146
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10537176
15.0%
4 10506329
15.0%
0 10475482
14.9%
3 6993937
10.0%
7 6993937
10.0%
2 3574086
 
5.1%
6 3574086
 
5.1%
9 3543239
 
5.0%
. 3512392
 
5.0%
5 3512392
 
5.0%
Other values (2) 6963090
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70186146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10537176
15.0%
4 10506329
15.0%
0 10475482
14.9%
3 6993937
10.0%
7 6993937
10.0%
2 3574086
 
5.1%
6 3574086
 
5.1%
9 3543239
 
5.0%
. 3512392
 
5.0%
5 3512392
 
5.0%
Other values (2) 6963090
9.9%

Traffic_Calming
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
-0.01886821470742019
3511142 
52.99918489939259
 
1250

Length

Max length20
Median length20
Mean length19.998932
Min length17

Characters and Unicode

Total characters70244090
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.01886821470742019
2nd row-0.01886821470742019
3rd row-0.01886821470742019
4th row-0.01886821470742019
5th row-0.01886821470742019

Common Values

ValueCountFrequency (%)
-0.01886821470742019 3511142
> 99.9%
52.99918489939259 1250
 
< 0.1%

Length

2024-11-27T17:10:08.323146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T17:10:08.412145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01886821470742019 3511142
> 99.9%
52.99918489939259 1250
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14044568
20.0%
8 10535926
15.0%
1 10534676
15.0%
2 7024784
10.0%
4 7023534
10.0%
7 7022284
10.0%
9 3519892
 
5.0%
. 3512392
 
5.0%
- 3511142
 
5.0%
6 3511142
 
5.0%
Other values (2) 3750
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63220556
90.0%
Other Punctuation 3512392
 
5.0%
Dash Punctuation 3511142
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14044568
22.2%
8 10535926
16.7%
1 10534676
16.7%
2 7024784
11.1%
4 7023534
11.1%
7 7022284
11.1%
9 3519892
 
5.6%
6 3511142
 
5.6%
5 2500
 
< 0.1%
3 1250
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 3512392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3511142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70244090
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14044568
20.0%
8 10535926
15.0%
1 10534676
15.0%
2 7024784
10.0%
4 7023534
10.0%
7 7022284
10.0%
9 3519892
 
5.0%
. 3512392
 
5.0%
- 3511142
 
5.0%
6 3511142
 
5.0%
Other values (2) 3750
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70244090
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14044568
20.0%
8 10535926
15.0%
1 10534676
15.0%
2 7024784
10.0%
4 7023534
10.0%
7 7022284
10.0%
9 3519892
 
5.0%
. 3512392
 
5.0%
- 3511142
 
5.0%
6 3511142
 
5.0%
Other values (2) 3750
 
< 0.1%

Traffic_Signal
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
-0.33555425835747843
3156932 
2.9801439710375037
355460 

Length

Max length20
Median length20
Mean length19.797597
Min length18

Characters and Unicode

Total characters69536920
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.33555425835747843
2nd row-0.33555425835747843
3rd row-0.33555425835747843
4th row-0.33555425835747843
5th row-0.33555425835747843

Common Values

ValueCountFrequency (%)
-0.33555425835747843 3156932
89.9%
2.9801439710375037 355460
 
10.1%

Length

2024-11-27T17:10:08.520146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T17:10:08.618160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.33555425835747843 3156932
89.9%
2.9801439710375037 355460
 
10.1%

Most occurring characters

ValueCountFrequency (%)
5 16140120
23.2%
3 13694108
19.7%
4 9826256
14.1%
7 7380244
10.6%
8 6669324
9.6%
0 4223312
 
6.1%
. 3512392
 
5.1%
2 3512392
 
5.1%
- 3156932
 
4.5%
9 710920
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62867596
90.4%
Other Punctuation 3512392
 
5.1%
Dash Punctuation 3156932
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 16140120
25.7%
3 13694108
21.8%
4 9826256
15.6%
7 7380244
11.7%
8 6669324
10.6%
0 4223312
 
6.7%
2 3512392
 
5.6%
9 710920
 
1.1%
1 710920
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 3512392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3156932
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69536920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 16140120
23.2%
3 13694108
19.7%
4 9826256
14.1%
7 7380244
10.6%
8 6669324
9.6%
0 4223312
 
6.1%
. 3512392
 
5.1%
2 3512392
 
5.1%
- 3156932
 
4.5%
9 710920
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69536920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 16140120
23.2%
3 13694108
19.7%
4 9826256
14.1%
7 7380244
10.6%
8 6669324
9.6%
0 4223312
 
6.1%
. 3512392
 
5.1%
2 3512392
 
5.1%
- 3156932
 
4.5%
9 710920
 
1.0%

Civil_Twilight
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
0.7139698745558026
2326468 
-1.400619319718708
1185924 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters63223056
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.7139698745558026
2nd row0.7139698745558026
3rd row0.7139698745558026
4th row-1.400619319718708
5th row0.7139698745558026

Common Values

ValueCountFrequency (%)
0.7139698745558026 2326468
66.2%
-1.400619319718708 1185924
33.8%

Length

2024-11-27T17:10:08.717146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T17:10:08.802145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.7139698745558026 2326468
66.2%
1.400619319718708 1185924
33.8%

Most occurring characters

ValueCountFrequency (%)
0 8210708
13.0%
1 7070164
11.2%
7 7024784
11.1%
9 7024784
11.1%
8 7024784
11.1%
5 6979404
11.0%
6 5838860
9.2%
. 3512392
5.6%
3 3512392
5.6%
4 3512392
5.6%
Other values (2) 3512392
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58524740
92.6%
Other Punctuation 3512392
 
5.6%
Dash Punctuation 1185924
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8210708
14.0%
1 7070164
12.1%
7 7024784
12.0%
9 7024784
12.0%
8 7024784
12.0%
5 6979404
11.9%
6 5838860
10.0%
3 3512392
6.0%
4 3512392
6.0%
2 2326468
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 3512392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1185924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63223056
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8210708
13.0%
1 7070164
11.2%
7 7024784
11.1%
9 7024784
11.1%
8 7024784
11.1%
5 6979404
11.0%
6 5838860
9.2%
. 3512392
5.6%
3 3512392
5.6%
4 3512392
5.6%
Other values (2) 3512392
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63223056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8210708
13.0%
1 7070164
11.2%
7 7024784
11.1%
9 7024784
11.1%
8 7024784
11.1%
5 6979404
11.0%
6 5838860
9.2%
. 3512392
5.6%
3 3512392
5.6%
4 3512392
5.6%
Other values (2) 3512392
5.6%

Duration_Seconds
Real number (ℝ)

Skewed 

Distinct1424948
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4505113 × 10-18
Minimum-0.035802414
Maximum106.62736
Zeros0
Zeros (%)0.0%
Negative3463635
Negative (%)98.6%
Memory size26.8 MiB
2024-11-27T17:10:08.924867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.035802414
5-th percentile-0.034016047
Q1-0.033873766
median-0.031875656
Q3-0.027786906
95-th percentile-0.011570166
Maximum106.62736
Range106.66317
Interquartile range (IQR)0.0060868595

Descriptive statistics

Standard deviation1.0000001
Coefficient of variation (CV)2.2469332 × 1017
Kurtosis2434.0988
Mean4.4505113 × 10-18
Median Absolute Deviation (MAD)0.0020070834
Skewness44.262669
Sum-1.0777512 × 10-10
Variance1.0000003
MonotonicityNot monotonic
2024-11-27T17:10:09.050746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.01157016606 286118
 
8.1%
-0.03487148717 114460
 
3.3%
-0.03284528534 80778
 
2.3%
-0.03385838625 56582
 
1.6%
-0.03352293728 42424
 
1.2%
-0.03183218442 37849
 
1.1%
-0.03149673545 25825
 
0.7%
-0.03387864827 15699
 
0.4%
-0.03387977394 15598
 
0.4%
-0.0338775226 15447
 
0.4%
Other values (1424938) 2821612
80.3%
ValueCountFrequency (%)
-0.03580241435 1
< 0.1%
-0.03568196791 2
< 0.1%
-0.03564819788 1
< 0.1%
-0.0356326182 1
< 0.1%
-0.03561442785 1
< 0.1%
-0.03554688778 2
< 0.1%
-0.03552196226 1
< 0.1%
-0.03551311775 2
< 0.1%
-0.03550414765 1
< 0.1%
-0.03550411241 1
< 0.1%
ValueCountFrequency (%)
106.6273649 1
< 0.1%
106.6107599 1
< 0.1%
106.6025226 1
< 0.1%
106.5934645 1
< 0.1%
106.5755263 1
< 0.1%
106.5720002 1
< 0.1%
106.5670211 1
< 0.1%
106.5625647 1
< 0.1%
106.5625343 1
< 0.1%
106.4871264 1
< 0.1%

cluster
Real number (ℝ)

Distinct10000
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9432514 × 10-17
Minimum-1.3042532
Maximum2.1592011
Zeros0
Zeros (%)0.0%
Negative1953159
Negative (%)55.6%
Memory size26.8 MiB
2024-11-27T17:10:09.165728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.3042532
5-th percentile-1.248486
Q1-0.90072045
median-0.19652977
Q30.78476497
95-th percentile1.8311792
Maximum2.1592011
Range3.4634543
Interquartile range (IQR)1.6854854

Descriptive statistics

Standard deviation1.0000001
Coefficient of variation (CV)1.0057074 × 1016
Kurtosis-0.97880873
Mean9.9432514 × 10-17
Median Absolute Deviation (MAD)0.80083072
Skewness0.48164241
Sum2.1827873 × 10-10
Variance1.0000003
MonotonicityNot monotonic
2024-11-27T17:10:09.282728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.248486036 15722
 
0.4%
-1.26961522 13831
 
0.4%
-1.211423369 10476
 
0.3%
-1.093654145 9629
 
0.3%
-1.123789211 7515
 
0.2%
-1.193757985 5571
 
0.2%
-1.181288303 4877
 
0.1%
-1.287280604 4825
 
0.1%
-1.087419304 4293
 
0.1%
-1.298018386 3876
 
0.1%
Other values (9990) 3431777
97.7%
ValueCountFrequency (%)
-1.304253227 168
 
< 0.1%
-1.303906847 294
 
< 0.1%
-1.303560467 1715
< 0.1%
-1.303214087 515
 
< 0.1%
-1.302867707 1078
< 0.1%
-1.302521327 483
 
< 0.1%
-1.302174947 822
< 0.1%
-1.301828567 229
 
< 0.1%
-1.301482187 1578
< 0.1%
-1.301135806 287
 
< 0.1%
ValueCountFrequency (%)
2.159201084 21
 
< 0.1%
2.158854704 156
< 0.1%
2.158508323 108
< 0.1%
2.158161943 113
< 0.1%
2.157815563 124
< 0.1%
2.157469183 132
< 0.1%
2.157122803 178
< 0.1%
2.156776423 88
 
< 0.1%
2.156430043 233
< 0.1%
2.156083663 186
< 0.1%

Severity
Categorical

Uniform 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
2
878098 
1
878098 
3
878098 
4
878098 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3512392
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 878098
25.0%
1 878098
25.0%
3 878098
25.0%
4 878098
25.0%

Length

2024-11-27T17:10:09.392843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T17:10:09.479902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 878098
25.0%
1 878098
25.0%
3 878098
25.0%
4 878098
25.0%

Most occurring characters

ValueCountFrequency (%)
2 878098
25.0%
1 878098
25.0%
3 878098
25.0%
4 878098
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3512392
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 878098
25.0%
1 878098
25.0%
3 878098
25.0%
4 878098
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3512392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 878098
25.0%
1 878098
25.0%
3 878098
25.0%
4 878098
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3512392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 878098
25.0%
1 878098
25.0%
3 878098
25.0%
4 878098
25.0%

Interactions

2024-11-27T17:09:50.915665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:08.842623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:13.263311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:17.969713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:22.601651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:28.023639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:32.747643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:37.368284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:41.862680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:46.518087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:51.384905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:09.275640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:13.710356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:18.429368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:23.047865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:28.555939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:33.201976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:37.810714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:42.340945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:46.950707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:51.848931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:09.717289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:14.165126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:18.896965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:23.552694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:29.052450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:33.674348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:38.250416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:42.806696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:47.410733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:52.301929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:10.136066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:14.713914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:19.362293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:24.014982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:29.513505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:34.117168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:38.696434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:43.277273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:47.843783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:52.768953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:10.583600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:15.186113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:19.833586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:24.510444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:29.954652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:34.583156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:39.132270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:43.755739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:48.281907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:53.225328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:11.008548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:15.663951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:20.300287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:25.033263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:30.407941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:35.020492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:39.574932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:44.223056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:48.722104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:53.703150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:11.445869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:16.127465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:20.773247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:25.585556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:30.887336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:35.490335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:40.005373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:44.704860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:49.163801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:54.161051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:11.880659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:16.595123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:21.236760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:26.105385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:31.345565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:35.955139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:40.454977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:45.149060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:49.606272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:54.619619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:12.369537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:17.045864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:21.708555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:26.798006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:31.826306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:36.430668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:40.907169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:45.615729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:50.012122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:55.064527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:12.808398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:17.518128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:22.157344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:27.483597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:32.282025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:36.891883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:41.353208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:46.066932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-27T17:09:50.439346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-11-27T17:10:09.567865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AmenityCivil_TwilightCrossingDistance(mi)Duration_SecondsGive_WayHumidity(%)JunctionNo_ExitPressure(in)RailwaySeverityStopTemperature(F)Traffic_CalmingTraffic_SignalVisibility(mi)Weather_ConditionWind_DirectionWind_Speed(mph)cluster
Amenity1.0000.0140.1220.0000.0010.0100.0110.0090.0220.0130.0570.0430.0380.0090.0500.0780.0020.0130.0140.0000.013
Civil_Twilight0.0141.0000.0670.0030.0130.0100.2500.0160.0070.0120.0090.2730.0130.2630.0030.0880.0170.1230.1410.0020.030
Crossing0.1220.0671.0000.0020.0060.0580.2130.0460.0500.0820.1290.1670.0880.1190.0330.5130.0050.0730.0370.0020.025
Distance(mi)0.0000.0030.0021.0000.3930.0000.0410.0020.0020.0350.0000.0060.000-0.2120.0040.004-0.069-0.021-0.0350.021-0.029
Duration_Seconds0.0010.0130.0060.3931.0000.0000.0050.0060.000-0.0590.0020.0220.002-0.0990.0000.008-0.032-0.032-0.029-0.028-0.032
Give_Way0.0100.0100.0580.0000.0001.0000.0050.0000.0110.0060.0040.0270.0360.0050.0040.0570.0070.0110.0050.0000.013
Humidity(%)0.0110.2500.2130.0410.0050.0051.0000.0170.0080.1200.0090.0920.013-0.3360.0030.212-0.4690.076-0.235-0.241-0.001
Junction0.0090.0160.0460.0020.0060.0000.0171.0000.0040.0540.0080.1680.0090.0340.0020.0610.0090.0490.0410.0000.015
No_Exit0.0220.0070.0500.0020.0000.0110.0080.0041.0000.0050.0060.0180.0250.0030.0330.0260.0120.0060.0060.0000.007
Pressure(in)0.0130.0120.0820.035-0.0590.0060.1200.0540.0051.0000.0100.1040.001-0.1120.0050.0940.0250.017-0.1070.017-0.055
Railway0.0570.0090.1290.0000.0020.0040.0090.0080.0060.0101.0000.0350.0120.0050.0040.0460.0030.0080.0200.0000.019
Severity0.0430.2730.1670.0060.0220.0270.0920.1680.0180.1040.0351.0000.0650.1700.0140.2130.0180.1480.0840.0010.065
Stop0.0380.0130.0880.0000.0020.0360.0130.0090.0250.0010.0120.0651.0000.0130.0200.0170.0040.0150.0190.0000.011
Temperature(F)0.0090.2630.119-0.212-0.0990.005-0.3360.0340.003-0.1120.0050.1700.0131.0000.0020.1280.2320.0730.1330.059-0.005
Traffic_Calming0.0500.0030.0330.0040.0000.0040.0030.0020.0330.0050.0040.0140.0200.0021.0000.0080.0080.0040.0060.0000.007
Traffic_Signal0.0780.0880.5130.0040.0080.0570.2120.0610.0260.0940.0460.2130.0170.1280.0081.0000.0060.0740.0350.0010.051
Visibility(mi)0.0020.0170.005-0.069-0.0320.007-0.4690.0090.0120.0250.0030.0180.0040.2320.0080.0061.000-0.1110.1170.065-0.011
Weather_Condition0.0130.1230.073-0.021-0.0320.0110.0760.0490.0060.0170.0080.1480.0150.0730.0040.074-0.1111.0000.0540.1160.005
Wind_Direction0.0140.1410.037-0.035-0.0290.005-0.2350.0410.006-0.1070.0200.0840.0190.1330.0060.0350.1170.0541.0000.3240.008
Wind_Speed(mph)0.0000.0020.0020.021-0.0280.000-0.2410.0000.0000.0170.0000.0010.0000.0590.0000.0010.0650.1160.3241.0000.014
cluster0.0130.0300.025-0.029-0.0320.013-0.0010.0150.007-0.0550.0190.0650.011-0.0050.0070.051-0.0110.0050.0080.0141.000

Missing values

2024-11-27T17:09:55.317059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-27T17:09:57.598547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Distance(mi)Temperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_SecondsclusterSeverity
0-0.2267731.0268590.3917480.4457250.33595-1.082175-0.341841-0.956781-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.026396-1.1546172
10.8179471.872758-1.2899450.3031620.335950.8308540.512094-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.033148-0.7704822
2-0.3018161.026859-0.4048430.1911470.33595-0.8908720.298610-0.956781-0.0632123.784243-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.030607-0.0669842
32.1618580.8012860.480258-0.1041630.33595-1.464781-1.622742-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.335554-1.400619-0.0197650.3649522
4-0.292497-0.157400-1.5112200.4049930.33595-0.699569-0.982292-0.736646-0.0632123.784243-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.025454-1.1913332
5-0.3116260.632106-0.3605880.5068240.335951.596066-0.128357-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.0328720.5104322
6-0.3116260.5193190.1704720.1707810.335951.0221570.9390611.162018-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.0188682.9801440.713970-0.0318471.5093922
7-0.293478-1.510839-0.7588840.9345150.33595-0.316963-0.982292-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.0297071.1543532
8-0.3116260.857679-0.1393130.4151760.335951.213460-0.3845381.162018-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.033891-1.2072672
9-0.3008350.462926-1.378455-3.6478910.335951.213460-0.982292-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.0319001.3206152
Distance(mi)Temperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_SecondsclusterSeverity
35123820.0540900.4484301.542379-0.3206330.226189-1.464781-1.604450-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.335554-1.400619-0.033948-0.9253134
35123830.566713-1.8274140.504798-1.0615050.335950-0.1256600.512094-0.956781-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.335554-1.400619-0.028793-0.8220924
35123840.798187-0.471467-0.7346830.1686450.3359501.4047630.514481-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.0277800.0199584
3512385-0.159026-0.6107840.9296320.1466910.335950-1.273478-1.487318-0.791680-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.0270650.0691444
35123860.022008-0.4525571.2217080.3506600.335950-0.5082660.070570-0.984298-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.335554-1.400619-0.011570-0.6360864
35123871.298817-1.743508-0.1578330.8270940.335950-1.4647810.017984-0.984298-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.335554-1.400619-0.0115701.4460054
35123881.8674290.7686800.606362-1.573816-1.9710400.4482480.6689041.987524-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.024489-0.8757814
35123890.109063-0.310644-0.459202-0.5159160.3359501.0221570.503971-0.901748-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.0275631.2672724
35123900.2008900.2037260.469374-0.4744890.335950-0.316963-0.281907-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.335554-1.400619-0.033943-1.2484864
3512391-0.081688-0.1616251.4429270.377036-2.298026-0.699569-0.0448231.189535-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.335554-1.400619-0.0115700.6046474

Duplicate rows

Most frequently occurring

Distance(mi)Temperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_SecondsclusterSeverity# duplicates
56-0.3116260.632106-1.2456890.1504150.335950-0.699569-0.128357-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.033523-0.684926129
27-0.311626-0.2701870.7900430.6086550.3359500.8308540.512094-0.956781-0.0632123.784243-0.044405-0.217566-0.030856-0.061178-0.094128-0.0188682.9801440.713970-0.0348711.726572115
76-0.3116261.139645-0.5376090.5271900.335950-1.082175-0.555324-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.034871-1.260956115
59-0.3116260.744892-1.688240-2.4157330.3359501.0221572.646930-0.736646-0.0632123.784243-0.044405-0.217566-0.030856-0.06117810.623792-0.0188682.980144-1.400619-0.032845-0.243291114
106-0.301816-0.7213330.9228080.4253590.3359500.6395510.085126-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.335554-1.400619-0.033523-0.783298114
984-0.0894390.6321061.0555730.3133450.335950-1.464781-1.622742-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.335554-1.400619-0.0335230.884176113
11-0.311626-0.6649400.6572780.506824-1.7709221.0221570.298610-0.956781-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.034871-0.221123112
12-0.311626-0.6649401.5423790.191147-3.772451-1.464781-1.622742-0.681613-0.0632123.784243-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.335554-1.400619-0.033881-0.727184112
77-0.3116261.139645-0.0950580.5577400.335950-0.5082660.298610-0.736646-0.063212-0.264254-0.044405-0.217566-0.030856-0.061178-0.094128-0.018868-0.3355540.713970-0.0335231.686046112
84-0.3116261.703578-2.617596-0.8882640.335950-1.464781-1.622742-0.736646-0.0632123.784243-0.044405-0.217566-0.030856-0.061178-0.094128-0.0188682.9801440.713970-0.031832-0.543256112